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Long memory continuous time models
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DOI：10.1016/0304-4076(95)01735-6.png)
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Abstract
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This paper presents a new family of long memory models: the continuous time moving average fractional process. The continuous time framework allows to reconcile two competitive types of modelling: fractional integration of ARMA processes and fractional Brownian Motion. A comparison with usual discrete time ARFIMA models is lead. Some well-known empirical evidence on macroeconomic and financial time series, such as variability of forward rates, aggregation of responses across heterogeneous agents, are well-captured by this continuous time modelling. Moreover, the usual statistical tools for long memory series and for Stochastic Differential Equations can be jointly applied in this setting.
Keywords:
long memory
continuous time models
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